Data science proficiency goes hand-in-hand with Ph.D-level research. For most of us, however, we don’t enter graduate school with strong programming skills. Instead, we’re likely thrown into a two-in-one, programming-and-statistical-methods course during our first year of graduate school, using any number of possible languages (MatLab, R, SPSS, etc.). Personally, I’m of the belief that learning R is invaluable. I think the learning curve is steeper compared to other languages, but as you develop proficiency and confidence, I find it to be a dynamic language that can do most-anything you’ll need within the scope of a Ph.D program. (A bonus: It’s heavily used in industry as well.)

Since proficiency comes with practice, I take coding workshops as frequently as they’re available. It solidifies what I already know and keeps me from forgetting methods that I may not use regularly when doing my own research and analyses. Sometimes, I’ll learn more efficient ways to do things too. Also, learning from different teachers has the benefit of having concepts explained in a different ways—things that seemed ‘fuzzy’ when explained by one professor may be crystal clear when explained in a different manner. 

I advocate for a general literacy across a few languages — after all, you’ll have little control over the format of materials sent over by colleagues — but here I thought I’d focus on my favorite beginner-level resources for R programming.